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authorab <abdullah_ahmed@brown.edu>2019-08-12 16:41:23 -0400
committerab <abdullah_ahmed@brown.edu>2019-08-12 16:41:23 -0400
commit9dd2a31b72e5e527e2dae3b68f856ab8da879e93 (patch)
tree09aaebd3e6845019c066fce4eb0f24133b2757cd /src/client/ClientRecommender.tsx
parent73ac98d53b80230bb085d71b61254f6c24a3e397 (diff)
documentation
Diffstat (limited to 'src/client/ClientRecommender.tsx')
-rw-r--r--src/client/ClientRecommender.tsx18
1 files changed, 10 insertions, 8 deletions
diff --git a/src/client/ClientRecommender.tsx b/src/client/ClientRecommender.tsx
index ddaa8a7fc..63f85c737 100644
--- a/src/client/ClientRecommender.tsx
+++ b/src/client/ClientRecommender.tsx
@@ -75,13 +75,15 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
const n = 200;
const num_words = paragraph.size;
let meanVector = new Array<number>(n).fill(0); // mean vector
- paragraph.forEach((wordvec: number[]) => {
- for (let i = 0; i < n; i++) {
- meanVector[i] += wordvec[i];
- }
- });
- meanVector = meanVector.map(x => x / num_words);
- this.addToDocSet(meanVector);
+ if (num_words > 0) { // check to see if paragraph actually was vectorized
+ paragraph.forEach((wordvec: number[]) => {
+ for (let i = 0; i < n; i++) {
+ meanVector[i] += wordvec[i];
+ }
+ });
+ meanVector = meanVector.map(x => x / num_words);
+ this.addToDocSet(meanVector);
+ }
return meanVector;
}
@@ -106,7 +108,7 @@ export class ClientRecommender extends React.Component<RecommenderProps> {
});
return keyterms;
};
- await CognitiveServices.Text.Manager.analyzer(extDoc, ["key words"], data, converter);
+ await CognitiveServices.Text.Appliers.analyzer(extDoc, ["key words"], data, converter);
}
/***